@conference{Somasundaran2018,
title = {Image denoising for image retrieval by cascading a deep quality assessment network},
author = {Biju Venkadath Somasundaran and Rajiv Soundararajan and Soma Biswas},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/08451132.pdf},
doi = {10.1109/ICIP.2018.8451132},
year = {2018},
date = {2018-09-06},
booktitle = {Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), 07.-10.10.18, Athens (Greece)},
abstract = {Image denoising algorithms have evolved to optimize image quality as measured according to human visual perception. However, image denoising to maximize the success of computer vision algorithms operating on the denoised image has been much less investigated. We consider the problem of image denoising for Gaussian noise with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising, allowing for optimization of the denoising CNN to maximize retrieval performance. This framework allows us to couple denoising to the retrieval problem. We show through experiments on noisy images of the Oxford and Paris buildings datasets that such an approach yields improved mean average precision when compared to using denoising methods that are oblivious to the task of image retrieval.},
keywords = {Student Research Grant},
pubstate = {published},
tppubtype = {conference}
}

Image denoising algorithms have evolved to optimize image quality as measured according to human visual perception. However, image denoising to maximize the success of computer vision algorithms operating on the denoised image has been much less investigated. We consider the problem of image denoising for Gaussian noise with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising, allowing for optimization of the denoising CNN to maximize retrieval performance. This framework allows us to couple denoising to the retrieval problem. We show through experiments on noisy images of the Oxford and Paris buildings datasets that such an approach yields improved mean average precision when compared to using denoising methods that are oblivious to the task of image retrieval.

@article{Kolathaya2018,
title = {Input-to-state safety with control barrier functions},
author = {Shishir Kolathaya and Aaron D. James},
url = {http://www.rbccps.org/wp-content/uploads/2018/08/08405547.pdf},
doi = {10.1109/LCSYS.2018.2853698},
year = {2018},
date = {2018-07-06},
journal = {IEEE Control Systems Letters},
volume = {3},
number = {1},
pages = {108-113},
abstract = {This letter presents a new notion of input-to-state safe control barrier functions (ISSf-CBFs), which ensure safety of nonlinear dynamical systems under input disturbances. Similar to how safety conditions are specified in terms of forward invariance of a set, input-to-state safety conditions are specified in terms of forward invariance of a slightly larger set. In this context, invariance of the larger set implies that the states stay either inside or very close to the smaller safe set; and this closeness is bounded by the magnitude of the disturbances. The main contribution of the letter is the methodology used for obtaining a valid ISSf-CBF, given a control barrier function. The associated universal control law will also be provided. Towards the end, we will study unified quadratic programs that combine control Lyapunov functions and ISSf-CBFs in order to obtain a single control law that ensures both safety and stability in systems with input disturbances.},
keywords = {Autonomous Systems},
pubstate = {published},
tppubtype = {article}
}

This letter presents a new notion of input-to-state safe control barrier functions (ISSf-CBFs), which ensure safety of nonlinear dynamical systems under input disturbances. Similar to how safety conditions are specified in terms of forward invariance of a set, input-to-state safety conditions are specified in terms of forward invariance of a slightly larger set. In this context, invariance of the larger set implies that the states stay either inside or very close to the smaller safe set; and this closeness is bounded by the magnitude of the disturbances. The main contribution of the letter is the methodology used for obtaining a valid ISSf-CBF, given a control barrier function. The associated universal control law will also be provided. Towards the end, we will study unified quadratic programs that combine control Lyapunov functions and ISSf-CBFs in order to obtain a single control law that ensures both safety and stability in systems with input disturbances.

@misc{Ramesh2018,
title = {Interoperable middleware for smartcities - Streetlighting on LoRaWAN as a case study},
author = {Rakshit Ramesh and Srikrishna Acharya and Vasanth Rajaraman and Arun Babu and Ashish Joglekar and Abhay Sharma and Bharadwaj Amrutur and Prashant Namekar},
url = {http://www.rbccps.org/wp-content/uploads/2018/08/AnInteroperableMiddleware.pdf},
year = {2018},
date = {2018-01-07},
booktitle = {10th International Conference on Communication Systems and Networks (COMSNETS), 03.-07.01.18, Bangalore},
abstract = {A smart city, is like every other organism a structured and organized entity. An organism comprises of a brain that performs its tasks via its appendages, just as smart cities should. However the brain needs a spine through which it communicates information to its appendages, becomes a point of confluence of different sensory and actuator pathways and facilitates activities. We herein put forth our work involving the building of such a spine for a smart city, and focus on the last mile communication aspects, especially using an LPWAN network and an implementation example with smart streetlights.},
keywords = {Smart City},
pubstate = {published},
tppubtype = {presentation}
}

A smart city, is like every other organism a structured and organized entity. An organism comprises of a brain that performs its tasks via its appendages, just as smart cities should. However the brain needs a spine through which it communicates information to its appendages, becomes a point of confluence of different sensory and actuator pathways and facilitates activities. We herein put forth our work involving the building of such a spine for a smart city, and focus on the last mile communication aspects, especially using an LPWAN network and an implementation example with smart streetlights.

@article{Kumaar2018,
title = {JuncNet: A deep neural network for road junction disambiguation for autonomous vehicles},
author = {Saumya Kumaar and B. Navaneethkrishnan and Sumedh Mannar and Subbarama N. Omkar},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/1809.01011.pdf},
year = {2018},
date = {2018-08-31},
journal = {arXiv: Computer Science},
abstract = {With a great amount of research going on in the field of autonomous vehicles or self-driving cars, there has been considerable progress in road detection and tracking algorithms. Most of these algorithms use GPS to handle road junctions and its subsequent decisions. However, there are places in the urban environment where it becomes difficult to get GPS fixes which render the junction decision handling erroneous or possibly risky. Vision-based junction detection, however, does not have such problems. This paper proposes a novel deep convolutional neural network architecture for disambiguation of junctions from roads with a high degree of accuracy. This network is benchmarked against other well known classifying network architectures like AlexNet and VGGnet. Further, we discuss a potential road navigation methodology which uses the proposed network model. We conclude by performing an experimental validation of the trained network and the navigational method on the roads of the Indian Institute of Science (IISc). },
keywords = {Autonomous Systems},
pubstate = {published},
tppubtype = {article}
}

With a great amount of research going on in the field of autonomous vehicles or self-driving cars, there has been considerable progress in road detection and tracking algorithms. Most of these algorithms use GPS to handle road junctions and its subsequent decisions. However, there are places in the urban environment where it becomes difficult to get GPS fixes which render the junction decision handling erroneous or possibly risky. Vision-based junction detection, however, does not have such problems. This paper proposes a novel deep convolutional neural network architecture for disambiguation of junctions from roads with a high degree of accuracy. This network is benchmarked against other well known classifying network architectures like AlexNet and VGGnet. Further, we discuss a potential road navigation methodology which uses the proposed network model. We conclude by performing an experimental validation of the trained network and the navigational method on the roads of the Indian Institute of Science (IISc).

@article{Vaidhiyan2018,
title = {Learning to detect an oddball target},
author = {Nidhin K. Vaidhiyan and Rajesh Sundaresan},
doi = {10.1109/TIT.2017.2778264},
year = {2018},
date = {2018-02-28},
journal = {IEEE Transactions on Information Theory},
volume = {64},
number = {2},
pages = {831-852},
abstract = {We consider the problem of detecting an odd process among a group of Poisson point processes, all having the same rate except the odd process. The actual rates of the odd and non-odd processes are unknown to the decision maker. We consider a time-slotted sequential detection scenario where, at the beginning of each slot, the decision maker can choose which process to observe during that time slot. We are interested in policies that satisfy a given constraint on the probability of false detection. We propose a variation on a sequential policy based on the generalised likelihood ratio statistic. The policy, via suitable thresholding, can be made to satisfy the given constraint on the probability of false detection. Furthermore, we show that the proposed policy is asymptotically optimal in terms of the conditional expected stopping time among all policies that satisfy the constraint on the probability of false detection. The asymptotic is as the probability of false detection is driven to zero. We apply our results to a particular visual search experiment studied recently by neuroscientists. Our model suggests a neuronal dissimilarity index for the visual search task. The neuronal dissimilarity index, when applied to visual search data from the particular experiment, correlates strongly with the behavioural data. However, the new dissimilarity index performs worse than some previously proposed neuronal dissimilarity indices. We explain why this may be attributed to some experiment conditions.},
keywords = {Electrical Communications Engineering},
pubstate = {published},
tppubtype = {article}
}

We consider the problem of detecting an odd process among a group of Poisson point processes, all having the same rate except the odd process. The actual rates of the odd and non-odd processes are unknown to the decision maker. We consider a time-slotted sequential detection scenario where, at the beginning of each slot, the decision maker can choose which process to observe during that time slot. We are interested in policies that satisfy a given constraint on the probability of false detection. We propose a variation on a sequential policy based on the generalised likelihood ratio statistic. The policy, via suitable thresholding, can be made to satisfy the given constraint on the probability of false detection. Furthermore, we show that the proposed policy is asymptotically optimal in terms of the conditional expected stopping time among all policies that satisfy the constraint on the probability of false detection. The asymptotic is as the probability of false detection is driven to zero. We apply our results to a particular visual search experiment studied recently by neuroscientists. Our model suggests a neuronal dissimilarity index for the visual search task. The neuronal dissimilarity index, when applied to visual search data from the particular experiment, correlates strongly with the behavioural data. However, the new dissimilarity index performs worse than some previously proposed neuronal dissimilarity indices. We explain why this may be attributed to some experiment conditions.

@conference{Ashwin2018,
title = {Mathematical model for pressure–deformation relationship of miniaturized McKibben actuators},
author = {K. P. Ashwin and Ashitava Ghosal},
doi = {10.1007/978-981-10-8597-0_23},
year = {2018},
date = {2018-08-29},
booktitle = {Proceedings of the 3rd International and 18th National Conference on Machines and Mechanisms (iNaCoMM), 13.-15.12.17, Mumbai},
journal = {Proceedings of the 3rd International and 18th National Conference on Machines and Mechanisms (iNaCoMM), 13.-15.12.17, Mumbai},
abstract = {A McKibben actuator/Pneumatic Artificial Muscle (PAM) is a soft actuator which has great potential in the field of bioinspired robotics. Miniaturized versions of PAMs or MPAMs of less than 1.5 mm diameter are ideal actuators for developing surgical devices due to their compliance and high power-to-weight ratio. Accurate mathematical model to represent the mechanics of PAM is an ongoing research. This paper develops a mathematical model which relates the input pressure to end-point deformation of a fabricated MPAM without external loading. The developed theoretical model is validated against experimental data for MPAM of lengths 60 and 70 mm. The model predicts the deformation of MPAM with standard error of less than 10%. The model is also able to predict the locking angle of 54.7∘ at higher pressures which is a distinct characteristic of McKibben actuators.},
keywords = {Walking Robot},
pubstate = {published},
tppubtype = {conference}
}

A McKibben actuator/Pneumatic Artificial Muscle (PAM) is a soft actuator which has great potential in the field of bioinspired robotics. Miniaturized versions of PAMs or MPAMs of less than 1.5 mm diameter are ideal actuators for developing surgical devices due to their compliance and high power-to-weight ratio. Accurate mathematical model to represent the mechanics of PAM is an ongoing research. This paper develops a mathematical model which relates the input pressure to end-point deformation of a fabricated MPAM without external loading. The developed theoretical model is validated against experimental data for MPAM of lengths 60 and 70 mm. The model predicts the deformation of MPAM with standard error of less than 10%. The model is also able to predict the locking angle of 54.7∘ at higher pressures which is a distinct characteristic of McKibben actuators.

This paper presents our method for enabling a UAV quadrotor, equipped with a monocular camera, to autonomously avoid collisions with obstacles in unstructured and unknown indoor environments. When compared to obstacle avoidance in ground vehicular robots, UAV navigation brings in additional challenges because the UAV motion is no more constrained to a well-defined indoor ground or street environment. Horizontal structures in indoor and outdoor environments like decorative items, furnishings, ceiling fans, sign-boards, tree branches etc., also become relevant obstacles unlike those for ground vehicular robots. Thus, methods of obstacle avoidance developed for ground robots are clearly inadequate for UAV navigation. Current control methods using monocular images for UAV obstacle avoidance are heavily dependent on environment information. These controllers do not fully retain and utilize the extensively available information about the ambient environment for decision making. We propose a deep reinforcement learning based method for UAV obstacle avoidance (OA) and autonomous exploration which is capable of doing exactly the same. The crucial idea in our method is the concept of partial observability and how UAVs can retain relevant information about the environment structure to make better future navigation decisions. Our OA technique uses recurrent neural networks with temporal attention and provides better results compared to prior works in terms of distance covered during navigation without collisions. In addition, our technique has a high inference rate (a key factor in robotic applications) and is energy-efficient as it minimizes oscillatory motion of UAV and reduces power wastage.

@article{Akhil2018,
title = {Network Utility Maximization Revisited: Three Issues and Their Resolution},
author = {P T, Akhil; Rajesh Sundaresan},
url = {http://cps.iisc.ac.in/wp-content/uploads/2019/09/Network-Utility-Maximization-Revisited-Three-issues-and-their-resolution.pdf},
year = {2018},
date = {2018-12-08},
journal = {arXiv.org: Mathematics, 2018},
abstract = {Distributed and iterative network utility maximization algorithms, such as the primal-dual algorithms or the network-user decomposition algorithms, often involve trajectories where the iterates may be infeasible, convergence to the optimal points of relaxed problems different from the original, or convergence to local maxima. In this paper, we highlight the three issues with iterative algorithms. We then propose a distributed and iterative algorithm that does not suffer from the three issues. In particular, we assert the feasibility of the algorithm's iterates at all times, convergence to global maximum of the given problem (rather than to global maximum of a relaxed problem), and avoidance of any associated spurious rest points of the dynamics. A benchmark algorithm due to Kelly, Maulloo and Tan (1998) [Rate control for communication networks: shadow prices, proportional fairness and stability, Journal of the Operational Research society, 49(3), 237-252] involves fast user updates coupled with slow network updates in the form of additive-increase multiplicative-decrease of suggested user flows. The proposed algorithm may be viewed as one with fast user updates and fast network updates that keeps the iterates feasible at all times. Simulations suggest that the convergence rate of the ordinary differential equation (ODE) tracked by our proposed algorithm's iterates is comparable to that of the ODE for the aforementioned benchmark algorithm.},
keywords = {Control and Optimisation},
pubstate = {published},
tppubtype = {article}
}

Distributed and iterative network utility maximization algorithms, such as the primal-dual algorithms or the network-user decomposition algorithms, often involve trajectories where the iterates may be infeasible, convergence to the optimal points of relaxed problems different from the original, or convergence to local maxima. In this paper, we highlight the three issues with iterative algorithms. We then propose a distributed and iterative algorithm that does not suffer from the three issues. In particular, we assert the feasibility of the algorithm's iterates at all times, convergence to global maximum of the given problem (rather than to global maximum of a relaxed problem), and avoidance of any associated spurious rest points of the dynamics. A benchmark algorithm due to Kelly, Maulloo and Tan (1998) [Rate control for communication networks: shadow prices, proportional fairness and stability, Journal of the Operational Research society, 49(3), 237-252] involves fast user updates coupled with slow network updates in the form of additive-increase multiplicative-decrease of suggested user flows. The proposed algorithm may be viewed as one with fast user updates and fast network updates that keeps the iterates feasible at all times. Simulations suggest that the convergence rate of the ordinary differential equation (ODE) tracked by our proposed algorithm's iterates is comparable to that of the ODE for the aforementioned benchmark algorithm.

@article{Diddigi2018,
title = {Novel sensor scheduling scheme for intruder tracking in energy efficient sensor networks},
author = {Raghuram Bharadwaj Diddigi and K. J. Prabuchandran and Shalabh Bhatnagar},
url = {http://www.rbccps.org/wp-content/uploads/2018/08/08314081.pdf},
doi = {10.1109/LWC.2018.2814576},
year = {2018},
date = {2018-03-12},
journal = {IEEE Wireless Communications Letters},
pages = {1-4},
abstract = {We consider the problem of tracking an intruder using a network of wireless sensors. For tracking the intruder at each instant, the optimal number and the right configuration of sensors has to be powered. As powering the sensors consumes energy, there is a trade off between accurately tracking the position of the intruder at each instant and the energy consumption of sensors. This problem has been formulated in the framework of Partially Observable Markov Decision Process (POMDP). Even for the state-of-the-art algorithm in the literature, the curse of dimensionality renders the problem intractable. In this paper, we formulate the Intrusion Detection (ID) problem with a suitable state-action space in the framework of POMDP and develop a Reinforcement Learning (RL) algorithm utilizing the Upper Confidence Tree Search (UCT) method to solve the ID problem. Through simulations, we show that our algorithm performs and scales well with the increasing state and action spaces.},
keywords = {Control and Optimisation},
pubstate = {published},
tppubtype = {article}
}

We consider the problem of tracking an intruder using a network of wireless sensors. For tracking the intruder at each instant, the optimal number and the right configuration of sensors has to be powered. As powering the sensors consumes energy, there is a trade off between accurately tracking the position of the intruder at each instant and the energy consumption of sensors. This problem has been formulated in the framework of Partially Observable Markov Decision Process (POMDP). Even for the state-of-the-art algorithm in the literature, the curse of dimensionality renders the problem intractable. In this paper, we formulate the Intrusion Detection (ID) problem with a suitable state-action space in the framework of POMDP and develop a Reinforcement Learning (RL) algorithm utilizing the Upper Confidence Tree Search (UCT) method to solve the ID problem. Through simulations, we show that our algorithm performs and scales well with the increasing state and action spaces.

@article{Jungers2018,
title = {Observability and controllability analysis of linear systems subject to data losses},
author = {Raphael M. Jungers and Atreyee Kundu and Maurice Heemels},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/08170282.pdf},
doi = {10.1109/TAC.2017.2781374},
year = {2018},
date = {2018-10-31},
journal = {IEEE Transactions on Automatic Control},
volume = {63},
number = {10},
pages = {3361-3376},
abstract = {We provide algorithmically verifiable necessary and sufficient conditions for fundamental system theoretic properties of discrete-time linear, systems subject to data losses. More precisely, the systems in our modeling framework are subject to disruptions (data losses) in the feedback loop, where the set of possible data loss sequences is captured by an automaton. As such, the results are applicable in the context of shared (wireless) communication networks and/or embedded architectures where some information on the data loss behavior is available a priori . We propose an algorithm for deciding observability (or the absence of it) for such systems, and show how this algorithm can be used also to decide other properties including constructibility, controllability, reachability, null-controllability, detectability, and stabilizability by means of relations that we establish among these properties. The main apparatus for our analysis is the celebrated Skolem's Theorem from Linear Algebra. Moreover, we study the relation between the model adopted in this paper and a previously introduced model where, instead of allowing dropouts in the feedback loop, one allows for time-varying delays.},
keywords = {Control and Optimisation},
pubstate = {published},
tppubtype = {article}
}

We provide algorithmically verifiable necessary and sufficient conditions for fundamental system theoretic properties of discrete-time linear, systems subject to data losses. More precisely, the systems in our modeling framework are subject to disruptions (data losses) in the feedback loop, where the set of possible data loss sequences is captured by an automaton. As such, the results are applicable in the context of shared (wireless) communication networks and/or embedded architectures where some information on the data loss behavior is available a priori . We propose an algorithm for deciding observability (or the absence of it) for such systems, and show how this algorithm can be used also to decide other properties including constructibility, controllability, reachability, null-controllability, detectability, and stabilizability by means of relations that we establish among these properties. The main apparatus for our analysis is the celebrated Skolem's Theorem from Linear Algebra. Moreover, we study the relation between the model adopted in this paper and a previously introduced model where, instead of allowing dropouts in the feedback loop, one allows for time-varying delays.

@article{Karanjkar2018c,
title = {On continuous-space embedding of discrete-parameter queueing systems},
author = {Neha Karanjkar and Madhav Desai and Shalabh Bhatnagar},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/1606.02900.pdf},
year = {2018},
date = {2018-02-12},
journal = {arXiv: Computer Science},
abstract = {Motivated by the problem of discrete-parameter simulation optimization (DPSO) of queueing systems, we consider the problem of embedding the discrete parameter space into a continuous one so that descent-based continuous-space methods could be directly applied for efficient optimization. We show that a randomization of the simulation model itself can be used to achieve such an embedding when the objective function is a long-run average measure. Unlike spatial interpolation, the computational cost of this embedding is independent of the number of parameters in the system, making the approach ideally suited to high-dimensional problems. We describe in detail the application of this technique to discrete-time queues for embedding queue capacities, number of servers and server-delay parameters into continuous space and empirically show that the technique can produce smooth interpolations of the objective function. Through an optimization case-study of a queueing network with 10^7 design points, we demonstrate that existing continuous optimizers can be effectively applied over such an embedding to find good solutions. },
keywords = {Control and Optimisation},
pubstate = {published},
tppubtype = {article}
}

Motivated by the problem of discrete-parameter simulation optimization (DPSO) of queueing systems, we consider the problem of embedding the discrete parameter space into a continuous one so that descent-based continuous-space methods could be directly applied for efficient optimization. We show that a randomization of the simulation model itself can be used to achieve such an embedding when the objective function is a long-run average measure. Unlike spatial interpolation, the computational cost of this embedding is independent of the number of parameters in the system, making the approach ideally suited to high-dimensional problems. We describe in detail the application of this technique to discrete-time queues for embedding queue capacities, number of servers and server-delay parameters into continuous space and empirically show that the technique can produce smooth interpolations of the objective function. Through an optimization case-study of a queueing network with 10^7 design points, we demonstrate that existing continuous optimizers can be effectively applied over such an embedding to find good solutions.

@conference{Mohit2018,
title = {On-Demand Augmentation of Contour Trees},
author = {Mohit, Sharma; Vijay Natarajan},
url = {http://cps.iisc.ac.in/wp-content/uploads/2019/09/On-Demand-Augmentation-of-Contour-Trees.pdf},
year = {2018},
date = {2018-12-10},
booktitle = {ICVGIP 2018:Proc. Indian Conference on Computer Vision, Graphics and Image Processing},
abstract = {The contour tree represents the topology of level sets of a scalar function. Nodes of the tree correspond to critical level sets and arcs of the tree represent a collection of topologically equivalent level sets connecting two critical level sets. The augmented contour tree contains degree-2 nodes on the arcs that represent regular level sets. The degree-2 nodes correspond to regular points of the scalar function and other critical points that do not affect the number of level set components. The augmented contour tree is significantly larger in size and requires more effort to compute when compared to the contour tree. Applications of the contour tree to data exploration and visualization require the augmented contour tree. Current approaches propose algorithms to compute the contour tree and the augmented contour tree from scratch. Precomputing and storing the large augmented contour tree will not be necessary if the contour tree can be augmented on-demand. This paper poses the problem of computing the augmented contour tree given a contour tree as input. Computational experiments demonstrate that the on-demand augmentation can be computed fast while resulting in good memory savings.},
keywords = {Student Research Grant},
pubstate = {published},
tppubtype = {conference}
}

The contour tree represents the topology of level sets of a scalar function. Nodes of the tree correspond to critical level sets and arcs of the tree represent a collection of topologically equivalent level sets connecting two critical level sets. The augmented contour tree contains degree-2 nodes on the arcs that represent regular level sets. The degree-2 nodes correspond to regular points of the scalar function and other critical points that do not affect the number of level set components. The augmented contour tree is significantly larger in size and requires more effort to compute when compared to the contour tree. Applications of the contour tree to data exploration and visualization require the augmented contour tree. Current approaches propose algorithms to compute the contour tree and the augmented contour tree from scratch. Precomputing and storing the large augmented contour tree will not be necessary if the contour tree can be augmented on-demand. This paper poses the problem of computing the augmented contour tree given a contour tree as input. Computational experiments demonstrate that the on-demand augmentation can be computed fast while resulting in good memory savings.

@conference{Joglekar2018b,
title = {Online I-V Tracer for per string monitoring and maintenance of PV panels},
author = {Ashish Joglekar and Balachandra Hegde},
url = {http://www.rbccps.org/wp-content/uploads/2019/05/08591616.pdf},
doi = {10.1109/IECON.2018.8591616},
year = {2018},
date = {2018-12-31},
booktitle = {Proceedings of the 44th Annual Conference of the IEEE Industrial Electronics Society (IECON), 21.-23.10.18, Washington, D.C. (USA)},
pages = {1890-1894},
abstract = {Large scale solar photovoltaic plants need to be monitored regularly for determining location and type of fault, cleaning schedules, operating point and performance. We propose a circuit that sits at the junction box level to provide `on demand', `online' I-V traces for series connected panels. This sparse sensing approach works on a per string basis and is therefore economical. The proposed online I-V tracer topology does not require the plant to be brought offline to obtain the I-V trace. The load's power requirement is met during the trace. The shape of the I-V trace helps determine the type of fault and localizes the fault to a specific string. The proposed solution has been tested in a practical field deployment. An analytics engine is also being developed to use the recorded I-V curve to provide optimal cleaning schedules, fault diagnosis and maintenance alerts.},
keywords = {Smart City},
pubstate = {published},
tppubtype = {conference}
}

Large scale solar photovoltaic plants need to be monitored regularly for determining location and type of fault, cleaning schedules, operating point and performance. We propose a circuit that sits at the junction box level to provide `on demand', `online' I-V traces for series connected panels. This sparse sensing approach works on a per string basis and is therefore economical. The proposed online I-V tracer topology does not require the plant to be brought offline to obtain the I-V trace. The load's power requirement is met during the trace. The shape of the I-V trace helps determine the type of fault and localizes the fault to a specific string. The proposed solution has been tested in a practical field deployment. An analytics engine is also being developed to use the recorded I-V curve to provide optimal cleaning schedules, fault diagnosis and maintenance alerts.

@article{Mayekar2018,
title = {Optimal source codes for timely updates},
author = {Prathamesh Mayekar and Parimal Parag and Himanshu Tyagi},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/1810.05561.pdf},
year = {2018},
date = {2018-10-12},
journal = {arXiv: Computer Science},
abstract = { A transmitter observing a sequence of independent and identically distributed random variables seeks to keep a receiver updated about its latest observations. The receiver need not be apprised about each symbol seen by the transmitter, but needs to output a symbol at each time instant t. If at time t the receiver outputs the symbol seen by the transmitter at time U(t)≤t, the age of information at the receiver at time t is t−U(t). We study the design of lossless source codes that enable transmission with minimum average age at the receiver. We show that the asymptotic minimum average age can be roughly attained by Shannon codes for a tilted version of the original pmf generating the symbols, which can be computed easily by solving an optimization problem. Furthermore, we exhibit an example with alphabet X where the Shannon codes for the original pmf incur an asymptotic average age of new{a factor O(√(log|X|))} more than that achieved by our codes. Underlying our prescription for optimal codes is a new variational formula for integer moments of random variables, which may be of independent interest. Also, we discuss possible extensions of our formulation to randomized schemes and erasure channel, and include a treatment of the related problem of source coding for minimum average queuing delay. },
keywords = {Electrical Communications Engineering},
pubstate = {published},
tppubtype = {article}
}

A transmitter observing a sequence of independent and identically distributed random variables seeks to keep a receiver updated about its latest observations. The receiver need not be apprised about each symbol seen by the transmitter, but needs to output a symbol at each time instant t. If at time t the receiver outputs the symbol seen by the transmitter at time U(t)≤t, the age of information at the receiver at time t is t−U(t). We study the design of lossless source codes that enable transmission with minimum average age at the receiver. We show that the asymptotic minimum average age can be roughly attained by Shannon codes for a tilted version of the original pmf generating the symbols, which can be computed easily by solving an optimization problem. Furthermore, we exhibit an example with alphabet X where the Shannon codes for the original pmf incur an asymptotic average age of new{a factor O(√(log|X|))} more than that achieved by our codes. Underlying our prescription for optimal codes is a new variational formula for integer moments of random variables, which may be of independent interest. Also, we discuss possible extensions of our formulation to randomized schemes and erasure channel, and include a treatment of the related problem of source coding for minimum average queuing delay.

@conference{Mukherjee2018,
title = {Phasesplit: A variable splitting framework for phase retrieval﻿},
author = {Subhadip Mukherjee and Suprosanna Shit and Chandra Sekhar Seelamantula},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/08461928.pdf},
doi = {10.1109/ICASSP.2018.8461928},
year = {2018},
date = {2018-09-13},
booktitle = {Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 15.-20.04.18, Calgary (Canada)},
abstract = {We develop two techniques based on alternating minimization and alternating directions method of multipliers for phase retrieval (PR) by employing a variable-splitting approach in a maximum likelihood estimation framework. This leads to an additional equality constraint, which is incorporated in the optimization framework using a quadratic penalty. Both algorithms are iterative, wherein the updates are computed in closed-form. Experimental results show that: (i) the proposed techniques converge faster than the state-of-the-art PR algorithms; (ii) the complexity is comparable to the state of the art; and (iii) the performance does not depend critically on the choice of the penalty parameter. We also show how sparsity can be incorporated within the variable splitting framework and demonstrate concrete applications to image reconstruction in frequency-domain optical-coherence tomography.},
keywords = {Student Research Grant},
pubstate = {published},
tppubtype = {conference}
}

We develop two techniques based on alternating minimization and alternating directions method of multipliers for phase retrieval (PR) by employing a variable-splitting approach in a maximum likelihood estimation framework. This leads to an additional equality constraint, which is incorporated in the optimization framework using a quadratic penalty. Both algorithms are iterative, wherein the updates are computed in closed-form. Experimental results show that: (i) the proposed techniques converge faster than the state-of-the-art PR algorithms; (ii) the complexity is comparable to the state of the art; and (iii) the performance does not depend critically on the choice of the penalty parameter. We also show how sparsity can be incorporated within the variable splitting framework and demonstrate concrete applications to image reconstruction in frequency-domain optical-coherence tomography.

We introduce deterministic perturbation schemes for the recently proposed random directions stochastic approximation (RDSA), and propose new first-order and second-order algorithms. In the latter case, these are the first second-order algorithms to incorporate deterministic perturbations. We show that the gradient and/or Hessian estimates in the resulting algorithms with deterministic perturbations are asymptotically unbiased, so that the algorithms are provably convergent. Furthermore, we derive convergence rates to establish the superiority of the first-order and second-order algorithms, for the special case of a convex and quadratic optimization problem, respectively. Numerical experiments are used to validate the theoretical results.

@article{George2018b,
title = {Random neuronal ensembles can inherently do context dependent coarse conjunctive encoding of input stimulus without any specific training},
author = {Jude Baby George and Grace Abraham and Zubin Rashid and Bharadwaj Amrutur and Sujit Sikdar},
url = {http://www.rbccps.org/wp-content/uploads/2018/06/41598_2018_Article_19462.pdf},
doi = {10.1038/s41598-018-19462-3},
year = {2018},
date = {2018-01-23},
journal = {Scientific Reports},
volume = {8},
pages = {1-10},
abstract = {Conjunctive encoding of inputs has been hypothesized to be a key feature in the computational capabilities of the brain. This has been inferred based on behavioral studies and electrophysiological recording from animals. In this report, we show that random neuronal ensembles grown on multi-electrode array perform a coarse-conjunctive encoding for a sequence of inputs with the first input setting the context. Such an encoding scheme creates similar yet unique population codes at the output of the ensemble, for related input sequences, which can then be decoded via a simple perceptron and hence a single STDP neuron layer. The random neuronal ensembles allow for pattern generalization and novel sequence classification without needing any specific learning or training of the ensemble. Such a representation of the inputs as population codes of neuronal ensemble outputs, has inherent redundancy and is suitable for further decoding via even probabilistic/random connections to subsequent neuronal layers. We reproduce this behavior in a mathematical model to show that a random neuronal network with a mix of excitatory and inhibitory neurons and sufficient connectivity creates similar coarse-conjunctive encoding of input sequences.},
keywords = {Electrical Communications Engineering},
pubstate = {published},
tppubtype = {article}
}

Conjunctive encoding of inputs has been hypothesized to be a key feature in the computational capabilities of the brain. This has been inferred based on behavioral studies and electrophysiological recording from animals. In this report, we show that random neuronal ensembles grown on multi-electrode array perform a coarse-conjunctive encoding for a sequence of inputs with the first input setting the context. Such an encoding scheme creates similar yet unique population codes at the output of the ensemble, for related input sequences, which can then be decoded via a simple perceptron and hence a single STDP neuron layer. The random neuronal ensembles allow for pattern generalization and novel sequence classification without needing any specific learning or training of the ensemble. Such a representation of the inputs as population codes of neuronal ensemble outputs, has inherent redundancy and is suitable for further decoding via even probabilistic/random connections to subsequent neuronal layers. We reproduce this behavior in a mathematical model to show that a random neuronal network with a mix of excitatory and inhibitory neurons and sufficient connectivity creates similar coarse-conjunctive encoding of input sequences.

@article{George2018b,
title = {Random neuronal networks show homeostatic regulation of global activity while showing persistent changes in specific connectivity paths to theta burst stimuli},
author = {Jude Baby George and Grace Abraham and Bharadwaj Amrutur and Sujit Sikdar},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/s41598-018-34634-x.pdf},
doi = {10.1038/s41598-018-34634-x},
year = {2018},
date = {2018-11-08},
journal = {Nature Scientific Reports},
abstract = {Learning in neuronal networks based on Hebbian principle has been shown to lead to destabilizing effects. Mechanisms have been identified that maintain homeostasis in such networks. However, the way in which these two opposing forces operate to support learning while maintaining stability is an active area of research. In this study, using neuronal networks grown on multi electrode arrays, we show that theta burst stimuli lead to persistent changes in functional connectivity along specific paths while the network maintains a global homeostasis. Simultaneous observations of spontaneous activity and stimulus evoked responses over several hours with theta burst training stimuli shows that global activity of the network quantified from spontaneous activity, which is disturbed due to theta burst stimuli is restored by homeostatic mechanisms while stimulus evoked changes in specific connectivity paths retain a memory trace of the training.},
keywords = {Remote Neonatal Monitoring and Intervention},
pubstate = {published},
tppubtype = {article}
}

Learning in neuronal networks based on Hebbian principle has been shown to lead to destabilizing effects. Mechanisms have been identified that maintain homeostasis in such networks. However, the way in which these two opposing forces operate to support learning while maintaining stability is an active area of research. In this study, using neuronal networks grown on multi electrode arrays, we show that theta burst stimuli lead to persistent changes in functional connectivity along specific paths while the network maintains a global homeostasis. Simultaneous observations of spontaneous activity and stimulus evoked responses over several hours with theta burst training stimuli shows that global activity of the network quantified from spontaneous activity, which is disturbed due to theta burst stimuli is restored by homeostatic mechanisms while stimulus evoked changes in specific connectivity paths retain a memory trace of the training.

@article{Singla2018,
title = {Realizing learned quadruped locomotion behaviors through kinematic motion primitives},
author = {Abhik Singla and Shounak Bhattacharya and Dhaivat Dholakiya and Shalabh Bhatnagar and Ashitava Ghosal and Bharadwaj Amrutur and Shishir Kolathaya},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/1810.03842.pdf},
year = {2018},
date = {2018-10-09},
journal = {arXiv: Computer Science},
abstract = {Humans and animals are believed to use a very minimal set of trajectories to perform a wide variety of tasks including walking. Our main objective in this paper is two fold 1) Obtain an effective tool to realize these basic motion patterns for quadrupedal walking, called the kinematic motion primitives (kMPs), via trajectories learned from deep reinforcement learning (D-RL) and 2) Realize a set of behaviors, namely trot, walk, gallop and bound from these kinematic motion primitives in our custom four legged robot, called the 'Stoch'. D-RL is a data driven approach, which has been shown to be very effective for realizing all kinds of robust locomotion behaviors, both in simulation and in experiment. On the other hand, kMPs are known to capture the underlying structure of walking and yield a set of derived behaviors. We first generate walking gaits from D-RL, which uses policy gradient based approaches. We then analyze the resulting walking by using principal component analysis. We observe that the kMPs extracted from PCA followed a similar pattern irrespective of the type of gaits generated. Leveraging on this underlying structure, we then realize walking in Stoch by a straightforward reconstruction of joint trajectories from kMPs. This type of methodology improves the transferability of these gaits to real hardware, lowers the computational overhead on-board, and also avoids multiple training iterations by generating a set of derived behaviors from a single learned gait. },
keywords = {Walking Robot},
pubstate = {published},
tppubtype = {article}
}

Humans and animals are believed to use a very minimal set of trajectories to perform a wide variety of tasks including walking. Our main objective in this paper is two fold 1) Obtain an effective tool to realize these basic motion patterns for quadrupedal walking, called the kinematic motion primitives (kMPs), via trajectories learned from deep reinforcement learning (D-RL) and 2) Realize a set of behaviors, namely trot, walk, gallop and bound from these kinematic motion primitives in our custom four legged robot, called the 'Stoch'. D-RL is a data driven approach, which has been shown to be very effective for realizing all kinds of robust locomotion behaviors, both in simulation and in experiment. On the other hand, kMPs are known to capture the underlying structure of walking and yield a set of derived behaviors. We first generate walking gaits from D-RL, which uses policy gradient based approaches. We then analyze the resulting walking by using principal component analysis. We observe that the kMPs extracted from PCA followed a similar pattern irrespective of the type of gaits generated. Leveraging on this underlying structure, we then realize walking in Stoch by a straightforward reconstruction of joint trajectories from kMPs. This type of methodology improves the transferability of these gaits to real hardware, lowers the computational overhead on-board, and also avoids multiple training iterations by generating a set of derived behaviors from a single learned gait.

Methods: Based on client needs and technological requirements, a wearable sensor device was designed and developed using principles of ‘social innovation’ design. The device underwent multiple iterations in product design and engineering based on user feedback, and then following preclinical testing, a techno- feasibility study and clinical trial were undertaken in a tertiary-care teaching hospital in Bangalore, India. Clinical trial phases I and IIa for evaluation of safety and efficacy were undertaken in the following sequence: 7 healthy adult volunteers; 18 healthy mothers; 3 healthy babies; 10 stable babies in the neonatal care intensive unit and 1 baby with morbidities. Time stamped skin temperature readings obtained at 5 min intervals over a 1-hour period from the device secured on upper arms of mothers and abdomen of neonates were compared against readings from thermometers used routinely in clinical practice.

Results: Devices were comfortably secured on to adults and neonates, and data were efficiently transmitted via the gateway device for secure storage and retrieval for analysis. The mean skin temperatures in mothers were lower than the axillary temperatures by 2°C; and in newborns, there was a precision of –0.5°C relative to axillary measurements. While occasional minimal adverse events were noted in healthy volunteers, no adverse events were noted in mothers or neonates.

Conclusions: This proof-of-concept study shows that this device is promising in terms of feasibility, safety and accuracy (with appropriate calibration) with potential for further refinements in device accuracy and pursuit of further phases of clinical research for improved maternal and neonatal health.},
keywords = {Remote Neonatal Monitoring and Intervention},
pubstate = {published},
tppubtype = {article}
}

Objective: Newer technologies such as wearables, sensors, mobile telephony and computing offer opportunities to monitor vital physiological parameters and tackle healthcare problems, thereby improving access and quality of care. We describe the design, development and testing of a wearable sensor device for remote biomonitoring of body temperatures in mothers and newborns in southern India.

Methods: Based on client needs and technological requirements, a wearable sensor device was designed and developed using principles of ‘social innovation’ design. The device underwent multiple iterations in product design and engineering based on user feedback, and then following preclinical testing, a techno- feasibility study and clinical trial were undertaken in a tertiary-care teaching hospital in Bangalore, India. Clinical trial phases I and IIa for evaluation of safety and efficacy were undertaken in the following sequence: 7 healthy adult volunteers; 18 healthy mothers; 3 healthy babies; 10 stable babies in the neonatal care intensive unit and 1 baby with morbidities. Time stamped skin temperature readings obtained at 5 min intervals over a 1-hour period from the device secured on upper arms of mothers and abdomen of neonates were compared against readings from thermometers used routinely in clinical practice.

Results: Devices were comfortably secured on to adults and neonates, and data were efficiently transmitted via the gateway device for secure storage and retrieval for analysis. The mean skin temperatures in mothers were lower than the axillary temperatures by 2°C; and in newborns, there was a precision of –0.5°C relative to axillary measurements. While occasional minimal adverse events were noted in healthy volunteers, no adverse events were noted in mothers or neonates.

Conclusions: This proof-of-concept study shows that this device is promising in terms of feasibility, safety and accuracy (with appropriate calibration) with potential for further refinements in device accuracy and pursuit of further phases of clinical research for improved maternal and neonatal health.

@conference{Prasanth2017,
title = {Safety analysis for integrated circuits in the context of hybrid systems},
author = {V. Prasanth and Rubin Parekhji and Bharadwaj Amrutur},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/08242045.pdf},
doi = {10.1109/TEST.2017.8242045},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 2017 International Test Conference (ITC), 31.10.-02.11.17, Fort Worth (USA)},
pages = {2378-2250},
abstract = {Many real-life systems have integrated circuits interacting with physical systems in safety critical applications. These systems are called hybrid systems. The safety analysis of integrated circuits used in such systems is typically done in isolation of the end application and associated physical system, and hence results in the need to take recourse to conservative design techniques utilizing costly redundancy. We are gradually moving away from the paradigm of independently designing the digital and physical parts of hybrid systems towards simultaneous considerations for both. These systems have an acceptable tolerance determined by the application due to the inertial nature of the physical system, error tolerance capability in closed loop applications, built-in hardware and software functionality, etc. In this paper, we perform a comparative study of integrated circuit safety analysis as practiced today and system level application specific safety analysis that incorporates a physical system. We propose an improved method based upon the divide and conquer approach for such co-analysis to address practical limitations associated with adopting system level analysis techniques during integrated circuit design. Experimental results for a representative motor control system indicate that the application has an error tolerance of 92-160 cycles of closed loop operation for worst case errors and a control value error tolerance in the range of 5-7% at different operating conditions. Incorporation of application tolerance results in up to 4.3X reduction in the number of hardware elements which need to be protected.},
keywords = {Electrical Communications Engineering},
pubstate = {published},
tppubtype = {conference}
}

Many real-life systems have integrated circuits interacting with physical systems in safety critical applications. These systems are called hybrid systems. The safety analysis of integrated circuits used in such systems is typically done in isolation of the end application and associated physical system, and hence results in the need to take recourse to conservative design techniques utilizing costly redundancy. We are gradually moving away from the paradigm of independently designing the digital and physical parts of hybrid systems towards simultaneous considerations for both. These systems have an acceptable tolerance determined by the application due to the inertial nature of the physical system, error tolerance capability in closed loop applications, built-in hardware and software functionality, etc. In this paper, we perform a comparative study of integrated circuit safety analysis as practiced today and system level application specific safety analysis that incorporates a physical system. We propose an improved method based upon the divide and conquer approach for such co-analysis to address practical limitations associated with adopting system level analysis techniques during integrated circuit design. Experimental results for a representative motor control system indicate that the application has an error tolerance of 92-160 cycles of closed loop operation for worst case errors and a control value error tolerance in the range of 5-7% at different operating conditions. Incorporation of application tolerance results in up to 4.3X reduction in the number of hardware elements which need to be protected.

@article{Maiya2018,
title = {Slum segmentation and change detection: A deep learning approach},
author = {Shishira R. Maiya and Sudharshan Chandra Babu},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/1811.07896.pdf},
year = {2018},
date = {2018-11-19},
journal = {arXiv: Computer Science},
abstract = {More than one billion people live in slums around the world. In some developing countries, slum residents make up for more than half of the population and lack reliable sanitation services, clean water, electricity, other basic services. Thus, slum rehabilitation and improvement is an important global challenge, and a significant amount of effort and resources have been put into this endeavor. These initiatives rely heavily on slum mapping and monitoring, and it is essential to have robust and efficient methods for mapping and monitoring existing slum settlements. In this work, we introduce an approach to segment and map individual slums from satellite imagery, leveraging regional convolutional neural networks for instance segmentation using transfer learning. In addition, we also introduce a method to perform change detection and monitor slum change over time. We show that our approach effectively learns slum shape and appearance, and demonstrates strong quantitative results, resulting in a maximum AP of 80.0. },
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {article}
}

More than one billion people live in slums around the world. In some developing countries, slum residents make up for more than half of the population and lack reliable sanitation services, clean water, electricity, other basic services. Thus, slum rehabilitation and improvement is an important global challenge, and a significant amount of effort and resources have been put into this endeavor. These initiatives rely heavily on slum mapping and monitoring, and it is essential to have robust and efficient methods for mapping and monitoring existing slum settlements. In this work, we introduce an approach to segment and map individual slums from satellite imagery, leveraging regional convolutional neural networks for instance segmentation using transfer learning. In addition, we also introduce a method to perform change detection and monitor slum change over time. We show that our approach effectively learns slum shape and appearance, and demonstrates strong quantitative results, resulting in a maximum AP of 80.0.

@article{Harshitha2018,
title = {Spatial awareness of a bacterial swarm},
author = {S. K. Harshitha and Shalini Harkar and Shubham Joge and Ayushi Mishra and Amith Zafal and Varsha Singh and Manoj Varma},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/1806.02995.pdf},
year = {2018},
date = {2018-06-08},
journal = {arXiv: Physics},
abstract = {Bacteria are perhaps the simplest living systems capable of complex behaviour involving sensing and coherent, collective behaviour an example of which is the phenomena of swarming on agar surfaces. Two fundamental questions in bacterial swarming is how the information gathered by individual members of the swarm is shared across the swarm leading to coordinated swarm behaviour and what specific advantages does membership of the swarm provide its members in learning about their environment. In this article, we show a remarkable example of the collective advantage of a bacterial swarm which enables it to sense inert obstacles along its path. Agent based computational model of swarming revealed that independent individual behaviour in response to a two-component signalling mechanism could produce such behaviour. This is striking because independent individual behaviour without any explicit communication between agents was found to be sufficient for the swarm to effectively compute the gradient of signalling molecule concentration across the swarm and respond to it. },
keywords = {Robot swarms},
pubstate = {published},
tppubtype = {article}
}

Bacteria are perhaps the simplest living systems capable of complex behaviour involving sensing and coherent, collective behaviour an example of which is the phenomena of swarming on agar surfaces. Two fundamental questions in bacterial swarming is how the information gathered by individual members of the swarm is shared across the swarm leading to coordinated swarm behaviour and what specific advantages does membership of the swarm provide its members in learning about their environment. In this article, we show a remarkable example of the collective advantage of a bacterial swarm which enables it to sense inert obstacles along its path. Agent based computational model of swarming revealed that independent individual behaviour in response to a two-component signalling mechanism could produce such behaviour. This is striking because independent individual behaviour without any explicit communication between agents was found to be sufficient for the swarm to effectively compute the gradient of signalling molecule concentration across the swarm and respond to it.

@article{Suran2018,
title = {Spatially resolved observation of water transport across nanomembranes using bright-field nanoscopy},
author = {Swathi Suran and Krishna Balasubramanian and Srinivasan Raghavan and Manoj Varma},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/1.5030082.pdf},
doi = {10.1063/1.5030082},
year = {2018},
date = {2018-07-25},
journal = {Applied Physics Letters},
volume = {113},
number = {4},
pages = {043701:1-5},
abstract = {Gaining a detailed understanding of water transport behavior through ultra-thin membranes including atomically thin graphene layers is increasingly becoming necessary due to their potential applications in water desalination and ion separation. It is important to correlate the nanoscopic architecture of the membrane with the macroscopic properties such as the average water transport rate and the ion selective transport rates. Such correlations are only possible when spatially resolved (in the lateral direction) information of mass transport across the membrane is available. Then, one will be able to identify the relative role of grain boundaries, defects, and other topographical structures of interest in determining the macroscopic parameters which will aid in optimizing the fabrication processes of such membranes. Current techniques do not provide spatially resolved information and only provide macroscopic parameters such as the bulk water transport rate. We describe a technique, referred to here as Bright-Field Nanoscopy (BFN), which provides a spatially resolved measurement of water transport across nanomembranes. Using this technique, we demonstrate how grain engineering of atomically thin chemical vapor deposited graphene membranes can tune the bulk water transport rate across the membranes by orders of magnitude. BFN exploits the strong thickness dependent color response of an optical stack consisting of a thin (∼25 nm) germanium film deposited over a gold substrate and only requires a regular bright-field microscope for data acquisition. To show the generality of this technique, we demonstrate the strong influence of the terminal layer on the bulk water transport rates in thin (∼20 nm) layer-by-layer deposited polyelectrolyte multilayer films by exploiting the spatially resolved nature of the acquired data. We also show that by controlling the ambient conditions, the effect of the terminal layer can be completely suppressed.},
keywords = {Sensing and Actuation Systems},
pubstate = {published},
tppubtype = {article}
}

Gaining a detailed understanding of water transport behavior through ultra-thin membranes including atomically thin graphene layers is increasingly becoming necessary due to their potential applications in water desalination and ion separation. It is important to correlate the nanoscopic architecture of the membrane with the macroscopic properties such as the average water transport rate and the ion selective transport rates. Such correlations are only possible when spatially resolved (in the lateral direction) information of mass transport across the membrane is available. Then, one will be able to identify the relative role of grain boundaries, defects, and other topographical structures of interest in determining the macroscopic parameters which will aid in optimizing the fabrication processes of such membranes. Current techniques do not provide spatially resolved information and only provide macroscopic parameters such as the bulk water transport rate. We describe a technique, referred to here as Bright-Field Nanoscopy (BFN), which provides a spatially resolved measurement of water transport across nanomembranes. Using this technique, we demonstrate how grain engineering of atomically thin chemical vapor deposited graphene membranes can tune the bulk water transport rate across the membranes by orders of magnitude. BFN exploits the strong thickness dependent color response of an optical stack consisting of a thin (∼25 nm) germanium film deposited over a gold substrate and only requires a regular bright-field microscope for data acquisition. To show the generality of this technique, we demonstrate the strong influence of the terminal layer on the bulk water transport rates in thin (∼20 nm) layer-by-layer deposited polyelectrolyte multilayer films by exploiting the spatially resolved nature of the acquired data. We also show that by controlling the ambient conditions, the effect of the terminal layer can be completely suppressed.

@article{Ramaswamy2018b,
title = {Stability of stochastic approximations with 'controlled Markov' noise and temporal difference learning},
author = {Arunselvan Ramaswamy and Shalabh Bhatnagar},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/084857411.pdf},
doi = {10.1109/TAC.2018.2874687},
year = {2018},
date = {2018-10-08},
journal = {IEEE Transactions on Automatic Control},
abstract = {We are interested in understanding stability (almost sure boundedness) of stochastic approximation algorithms (SAs) driven by a "controlled Markov" process. Analyzing this class of algorithms is important, since many reinforcement learning (RL) algorithms can be cast as SAs driven by a "controlled Markov" process. In this paper, we present easily verifiable sufficient conditions for stability and convergence of SAs driven by a "controlled Markov" process. Many RL applications involve continuous state spaces. While our analysis readily ensures stability for such continuous state applications, traditional analyses do not. As compared to literature, our analysis presents a two-fold generalization: (a) the Markov process may evolve in a continuous state space and (b) the process need not be ergodic under any given stationary policy. Temporal difference learning (TD) is an important policy evaluation method in reinforcement learning. The theory developed herein, is used to analyze generalized TD(0), an important variant of TD. Our theory is also used to analyze a TD formulation of supervised learning for forecasting problems.},
keywords = {Control and Optimisation},
pubstate = {published},
tppubtype = {article}
}

We are interested in understanding stability (almost sure boundedness) of stochastic approximation algorithms (SAs) driven by a "controlled Markov" process. Analyzing this class of algorithms is important, since many reinforcement learning (RL) algorithms can be cast as SAs driven by a "controlled Markov" process. In this paper, we present easily verifiable sufficient conditions for stability and convergence of SAs driven by a "controlled Markov" process. Many RL applications involve continuous state spaces. While our analysis readily ensures stability for such continuous state applications, traditional analyses do not. As compared to literature, our analysis presents a two-fold generalization: (a) the Markov process may evolve in a continuous state space and (b) the process need not be ergodic under any given stationary policy. Temporal difference learning (TD) is an important policy evaluation method in reinforcement learning. The theory developed herein, is used to analyze generalized TD(0), an important variant of TD. Our theory is also used to analyze a TD formulation of supervised learning for forecasting problems.

This paper deals with input/output-to-state stability (IOSS) of continuous-time switched nonlinear systems under restricted switching. The switching signals obey restrictions on: (i) transitions between subsystems, and (ii) dwell time on subsystems. Given a family of systems, possibly containing non-IOSS dynamics, we present an algorithm to construct a time-dependent switching signal that guarantees IOSS of the resulting switched system under these restrictions. The main apparatus for our analysis are multiple Lyapunov-like functions and graph-theoretic tools.

@article{Yaji2018,
title = {Stochastic recursive inclusions with non-additive iterate-dependent Markov noise},
author = {Vinayaka G. Yaji and Shalabh Bhatnagar},
doi = {10.1080/17442508.2017.1353984},
year = {2018},
date = {2018-03-30},
journal = {Stochastics. An International Journal of Probability and Stochastic Processes},
volume = {90},
number = {3},
pages = {330-363},
abstract = {In this paper we study the asymptotic behaviour of stochastic approximation schemes with set-valued drift function and non-additive iterate-dependent Markov noise. We show that a linearly interpolated trajectory of such a recursion is an asymptotic pseudotrajectory for the flow of a limiting differential inclusion obtained by averaging the set-valued drift function of the recursion w.r.t. the stationary distributions of the Markov noise. The limit set theorem by Benaim is then used to characterize the limit sets of the recursion in terms of the dynamics of the limiting differential inclusion. We then state two variants of the Markov noise assumption under which the analysis of the recursion is similar to the one presented in this paper. Scenarios where our recursion naturally appears are presented as applications. These include controlled stochastic approximation, subgradient descent, approximate drift problem and analysis of discontinuous dynamics all in the presence of non-additive iterate-dependent Markov noise.},
keywords = {Control and Optimisation},
pubstate = {published},
tppubtype = {article}
}

In this paper we study the asymptotic behaviour of stochastic approximation schemes with set-valued drift function and non-additive iterate-dependent Markov noise. We show that a linearly interpolated trajectory of such a recursion is an asymptotic pseudotrajectory for the flow of a limiting differential inclusion obtained by averaging the set-valued drift function of the recursion w.r.t. the stationary distributions of the Markov noise. The limit set theorem by Benaim is then used to characterize the limit sets of the recursion in terms of the dynamics of the limiting differential inclusion. We then state two variants of the Markov noise assumption under which the analysis of the recursion is similar to the one presented in this paper. Scenarios where our recursion naturally appears are presented as applications. These include controlled stochastic approximation, subgradient descent, approximate drift problem and analysis of discontinuous dynamics all in the presence of non-additive iterate-dependent Markov noise.

@conference{Karthik2018,
title = {Subband selection for binaural speech source localization},
author = {Girija Ramesan Karthik and Prasanta Kumar Ghosh},
url = {http://www.rbccps.org/wp-content/uploads/2018/08/c330f2de2a9b57bad9b268c85d9a77b1742d.pdf},
doi = {10.21437/Interspeech.2017-954},
year = {2018},
date = {2018-02-28},
booktitle = {Proceedings of the 18th Annual Conference of the International Speech Communication Association (INTERSPEECH): Situated Interaction, 20.-24.08.17, Stockholm (Sweden)},
pages = {1929-1933},
abstract = {We consider the task of speech source localization using bin- aural cues, namely interaural time and level difference (ITD & ILD). A typical approach is to process binaural speech us- ing gammatone filters and calculate frame-level ITD and ILD in each subband. The ITD, ILD and their combination (ITLD) in each subband are statistically modelled using Gaussian mix- ture models for every direction during training. Given a binaural test-speech, the source is localized using maximum likelihood criterion assuming that the binaural cues in each subband are in- dependent. We, in this work, investigate the robustness of each subband for localization and compare their performance against the full-band scheme with 32 gammatone filters. We propose a subband selection procedure using the training data where sub- bands are rank ordered based on their localization performance. Experiments on Subject 003 from the CIPIC database reveal that, for high SNRs, the ITD and ITLD of just one subband centered at 296Hz is sufficient to yield localization accuracy identical to that of the full-band scheme with a test-speech of duration 1sec. At low SNRs, in case of ITD, the selected sub- bands are found to perform better than the full-band scheme.},
keywords = {Student Research Grant},
pubstate = {published},
tppubtype = {conference}
}

We consider the task of speech source localization using bin- aural cues, namely interaural time and level difference (ITD & ILD). A typical approach is to process binaural speech us- ing gammatone filters and calculate frame-level ITD and ILD in each subband. The ITD, ILD and their combination (ITLD) in each subband are statistically modelled using Gaussian mix- ture models for every direction during training. Given a binaural test-speech, the source is localized using maximum likelihood criterion assuming that the binaural cues in each subband are in- dependent. We, in this work, investigate the robustness of each subband for localization and compare their performance against the full-band scheme with 32 gammatone filters. We propose a subband selection procedure using the training data where sub- bands are rank ordered based on their localization performance. Experiments on Subject 003 from the CIPIC database reveal that, for high SNRs, the ITD and ITLD of just one subband centered at 296Hz is sufficient to yield localization accuracy identical to that of the full-band scheme with a test-speech of duration 1sec. At low SNRs, in case of ITD, the selected sub- bands are found to perform better than the full-band scheme.

@article{Kumar2018d,
title = {Sustained high evaporation rates from porous media consisting of packed circular rods},
author = {Navneet Kumar and Jaywant H. Arakeri},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/1-s2.0-S1290072917320045-main.pdf},
doi = {10.1016/j.ijthermalsci.2018.07.035},
year = {2018},
date = {2018-11-30},
journal = {International Journal of Thermal Sciences},
volume = {133},
pages = {299-306},
abstract = {Studies of drying from a conventional porous medium, consisting of spheres, have shown the existence of three periods. In the first period evaporation rate is high and essentially depends on the atmospheric demand. Relatively simpler geometry, such as polygonal capillaries, pins liquid along the corner and retains high evaporation rate till a certain extent. We report an experimental study of evaporation from a new, but yet, simpler rod-based porous medium (RBPM) consisting of closely packed vertical circular rods. This configuration can be thought of an ‘extreme case’ of a polygonal capillary where the internal angle is zero (0^0). Infrared heating at about 1000 W/m^2 causes evaporation from an initially saturated RBPM kept in an acrylic box. We find sustained high evaporation rates until almost all the water is depleted, a feature very different from either a conventional porous medium or a polygonal capillary. Near-zero radii contacts between the rods are able to source the liquid, against gravity, to the open end throughout the rod length (75 mm) and thus capillary depinning in all the experiments were forced due to limited liquid content. Using a novel fluorescein dye visualization technique and a simple mathematical model, we show that the corner films present in the near-zero radii contacts between rods results in the high sustained evaporation rate.},
keywords = {Student Research Grant},
pubstate = {published},
tppubtype = {article}
}

Studies of drying from a conventional porous medium, consisting of spheres, have shown the existence of three periods. In the first period evaporation rate is high and essentially depends on the atmospheric demand. Relatively simpler geometry, such as polygonal capillaries, pins liquid along the corner and retains high evaporation rate till a certain extent. We report an experimental study of evaporation from a new, but yet, simpler rod-based porous medium (RBPM) consisting of closely packed vertical circular rods. This configuration can be thought of an ‘extreme case’ of a polygonal capillary where the internal angle is zero (0^0). Infrared heating at about 1000 W/m^2 causes evaporation from an initially saturated RBPM kept in an acrylic box. We find sustained high evaporation rates until almost all the water is depleted, a feature very different from either a conventional porous medium or a polygonal capillary. Near-zero radii contacts between the rods are able to source the liquid, against gravity, to the open end throughout the rod length (75 mm) and thus capillary depinning in all the experiments were forced due to limited liquid content. Using a novel fluorescein dye visualization technique and a simple mathematical model, we show that the corner films present in the near-zero radii contacts between rods results in the high sustained evaporation rate.

@article{Kadavankandy2018,
title = {The power of side-information in subgraph detection},
author = {Arun Kadavankandy and Konstantin Avrachenkov and Laura Cottatellucci and Rajesh Sundaresan},
doi = {10.1109/TSP.2017.2786266},
year = {2018},
date = {2018-04-02},
journal = {IEEE Transactions on Signal Processing},
volume = {66},
number = {7},
pages = {1905-1919},
abstract = {In this paper, we tackle the problem of hidden community detection. We consider belief propagation (BP) applied to the problem of detecting a hidden Erdös-Rényi (ER) graph embedded in a larger and sparser ER graph, in the presence of side-information. We derive two related algorithms based on BP to perform subgraph detection in the presence of two kinds of side-information. The first variant of side-information consists of a set of nodes, called cues, known to be from the subgraph. The second variant of side-information consists of a set of nodes that are cues with a given probability. It was shown in past works that BP without side-information fails to detect the subgraph correctly when a so-called effective signal-to-noise ratio parameter falls below a threshold. In contrast, in the presence of nontrivial side-information, we show that the BP algorithm achieves asymptotically zero error for any value of a suitably defined phase-transition parameter. We validate our results on synthetic datasets and a few real world networks.},
keywords = {Electrical Communications Engineering},
pubstate = {published},
tppubtype = {article}
}

In this paper, we tackle the problem of hidden community detection. We consider belief propagation (BP) applied to the problem of detecting a hidden Erdös-Rényi (ER) graph embedded in a larger and sparser ER graph, in the presence of side-information. We derive two related algorithms based on BP to perform subgraph detection in the presence of two kinds of side-information. The first variant of side-information consists of a set of nodes, called cues, known to be from the subgraph. The second variant of side-information consists of a set of nodes that are cues with a given probability. It was shown in past works that BP without side-information fails to detect the subgraph correctly when a so-called effective signal-to-noise ratio parameter falls below a threshold. In contrast, in the presence of nontrivial side-information, we show that the BP algorithm achieves asymptotically zero error for any value of a suitably defined phase-transition parameter. We validate our results on synthetic datasets and a few real world networks.

@article{Simmhan2018,
title = {Towards a data‐driven IoT software architecture for Smart City utilities},
author = {Yogesh Simmhan and Pushkara Ravindra and Shilpa Chaturvedi and Malati Hedge and Rashmi Ballamajalu},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/Simmhan_et_al-2018-Software3A_Practice_and_Experience.pdf},
doi = {10.1002/spe.2580},
year = {2018},
date = {2018-07-31},
journal = {Software: Practice and Experience},
volume = {48},
number = {7},
pages = {1390-1416},
abstract = {The Internet of things (IoT) is emerging as the next big wave of digital presence for billions of devices on the Internet. Smart cities are a practical manifestation of IoT, with the goal of efficient, reliable, and safe delivery of city utilities like water, power, and transport to residents, through their intelligent management. A data‐driven IoT software platform is essential for realizing manageable and sustainable smart utilities and for novel applications to be developed upon them. Here, we propose such service‐oriented software architecture to address 2 key operational activities in a smart utility: the IoT fabric for resource management and the data and application platform for decision‐making. Our design uses Open Web standards and evolving network protocols, cloud and edge resources, and streaming big data platforms. We motivate our design requirements using the smart water management domain; some of these requirements are unique to developing nations. We also validate the architecture within a campus‐scale IoT testbed at the Indian Institute of Science, Bangalore and present our experiences. Our architecture is scalable to a township or city while also generalizable to other smart utility domains. Our experiences serve as a template for other similar efforts, particularly in emerging markets and highlight the gaps and opportunities for a data‐driven IoT software architecture for smart cities. },
keywords = {Smart City},
pubstate = {published},
tppubtype = {article}
}

The Internet of things (IoT) is emerging as the next big wave of digital presence for billions of devices on the Internet. Smart cities are a practical manifestation of IoT, with the goal of efficient, reliable, and safe delivery of city utilities like water, power, and transport to residents, through their intelligent management. A data‐driven IoT software platform is essential for realizing manageable and sustainable smart utilities and for novel applications to be developed upon them. Here, we propose such service‐oriented software architecture to address 2 key operational activities in a smart utility: the IoT fabric for resource management and the data and application platform for decision‐making. Our design uses Open Web standards and evolving network protocols, cloud and edge resources, and streaming big data platforms. We motivate our design requirements using the smart water management domain; some of these requirements are unique to developing nations. We also validate the architecture within a campus‐scale IoT testbed at the Indian Institute of Science, Bangalore and present our experiences. Our architecture is scalable to a township or city while also generalizable to other smart utility domains. Our experiences serve as a template for other similar efforts, particularly in emerging markets and highlight the gaps and opportunities for a data‐driven IoT software architecture for smart cities.

@conference{Kumar2018bb,
title = {Towards a portable human gait analysis and monitoring system},
author = {Sandeep Kumar and K. Gopinath and Laura Rocchi and Poorna Talked Sukumar and Suyameendra Kulkarni and Jayanth Sampath},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/08372660.pdf},
doi = {10.1109/ICSIGSYS.2018.8372660},
year = {2018},
date = {2018-06-07},
booktitle = {Proceedings of the 2018 International Conference on Signals and Systems (ICSigSys), 01.-03.05.18, Bali (Indonesia)},
abstract = {Human Gait analysis is useful in many cases, such as, detecting the underlying cause of an abnormal gait, rehabilitation of subjects suffering from motor related diseases such as Parkinson's disease or Cerebral Palsy, improving the athletic performance of sports person etc. However, gait analysis has seen limited usage, especially in developing countries, because of the high cost involved in setting up a gait laboratory. We present a portable gait analysis system using Inertial Measurement Unit (IMU) sensors to collect movement data and a Smart-phone to process it. IMU sensors has gained significant popularity in the last few years as viable option for gait analysis because its low cost, small size and ease of use. Using the accelerometer and gyroscope data from 3 EXL-S3 IMU sensors (on thigh, shank and foot), we measure kinematic angles in the sagittal plane and detect Heel Strike (HT) and Toe Off (TO) events using methods based on [11] and [4] respectively. To measure the accuracy of our system, we compare it with an Optical Gait Analysis system, which is the current gold standard for gait analysis 1 . We measure the gait parameters for 3 healthy individuals belonging to different age group and achieve an RMSE of 4.739° ± 1.961°, 3.7° ± 3.02° and 4.12° ± 1.21° for Knee Flexion Extension, Ankle Dorsi Flexion respectively and Hip Flexion Extension respectively. We measure the Heel Strike and Toe Off using shank and foot mounted sensor independently. 34.5 ± 28.3 ms and 27.5 ± 32.8 ms is the RMSE for HT calculated by shank and foot sensor w.r.t. optical system respectively. The RMSE for Toe Off is 36.2 ± 36.8 ms and 37.5 ± 35.9 ms for shank and foot sensor w.r.t. optical system respectively.},
keywords = {Sensor System for Monitoring Stroke Patients in Rehabilitation},
pubstate = {published},
tppubtype = {conference}
}

Human Gait analysis is useful in many cases, such as, detecting the underlying cause of an abnormal gait, rehabilitation of subjects suffering from motor related diseases such as Parkinson's disease or Cerebral Palsy, improving the athletic performance of sports person etc. However, gait analysis has seen limited usage, especially in developing countries, because of the high cost involved in setting up a gait laboratory. We present a portable gait analysis system using Inertial Measurement Unit (IMU) sensors to collect movement data and a Smart-phone to process it. IMU sensors has gained significant popularity in the last few years as viable option for gait analysis because its low cost, small size and ease of use. Using the accelerometer and gyroscope data from 3 EXL-S3 IMU sensors (on thigh, shank and foot), we measure kinematic angles in the sagittal plane and detect Heel Strike (HT) and Toe Off (TO) events using methods based on [11] and [4] respectively. To measure the accuracy of our system, we compare it with an Optical Gait Analysis system, which is the current gold standard for gait analysis 1 . We measure the gait parameters for 3 healthy individuals belonging to different age group and achieve an RMSE of 4.739° ± 1.961°, 3.7° ± 3.02° and 4.12° ± 1.21° for Knee Flexion Extension, Ankle Dorsi Flexion respectively and Hip Flexion Extension respectively. We measure the Heel Strike and Toe Off using shank and foot mounted sensor independently. 34.5 ± 28.3 ms and 27.5 ± 32.8 ms is the RMSE for HT calculated by shank and foot sensor w.r.t. optical system respectively. The RMSE for Toe Off is 36.2 ± 36.8 ms and 37.5 ± 35.9 ms for shank and foot sensor w.r.t. optical system respectively.

We present for the first time an asymptotic convergence analysis of two time-
scale stochastic approximation driven by “controlled” Markov noise. In particular, the faster and slower recursions have nonadditive controlled Markov noise components in addition to martingale difference noise. We analyze the asymptotic behavior of our framework by relating it to limiting differential inclusions in both time scales that are defined in terms of the ergodic occupation measures associated with the controlled Markov processes. Finally, we present a solution to the off-policy convergence problem for temporal-difference learning with linear function approximation, using our results.

@article{Karthika2018,
title = {Understanding crowd dynamics at Ghat regions during world's largest mass religious gathering, Kumbh Mela},
author = {P. S. Karthika and P. M. Aparna and Ashish Verma},
doi = {10.1016/j.ijdrr.2018.08.005},
year = {2018},
date = {2018-10-01},
journal = {International Journal of Disaster Risk Reduction},
volume = {31},
pages = {918-925},
abstract = {In this paper a porous flow approach on a cul de sac is proposed to understand the dynamics of crowd at ghat regions (banks of the sacred river) in mass religious gatherings. Kumbh Mela, one of the mankind's largest religious gathering encompassing almost all possible crowd scenarios, provides a unique opportunity to explore the crowd dynamics along all facets. Here, Cul-de-sac refers to the ghat region where people gather with the intention to take holy dip. The data used for this study was collected during Kumbh Mela held during 22nd April to 21st May 2016. Visual observations from the video data shows a high degree of complexity probably due to the nature of activities at the study location, e.g., lane formation, creeping behavior etc. The proposed porous flow approach divides the entire study area into pores, and it is assumed that pilgrims traverse this network through interconnected vacant pores. The pedestrian data from video sequences (entry time, exit time, and flow) is extracted manually and time series analysis of pore occupancy is done to get an approximate measure of local density. Further, using Poisson regression analysis it was found that both the inflow and the duration of holy dip are significant factors in influencing the number of arrivals into the pore. Since behavioral aspects of a pedestrian is a significant governing factor of crowd dynamics, these microscopic parameters can be used to get a measure of criticality of the system in terms of crowd risk.},
keywords = {The Kumbh Mela Experiment},
pubstate = {published},
tppubtype = {article}
}

In this paper a porous flow approach on a cul de sac is proposed to understand the dynamics of crowd at ghat regions (banks of the sacred river) in mass religious gatherings. Kumbh Mela, one of the mankind's largest religious gathering encompassing almost all possible crowd scenarios, provides a unique opportunity to explore the crowd dynamics along all facets. Here, Cul-de-sac refers to the ghat region where people gather with the intention to take holy dip. The data used for this study was collected during Kumbh Mela held during 22nd April to 21st May 2016. Visual observations from the video data shows a high degree of complexity probably due to the nature of activities at the study location, e.g., lane formation, creeping behavior etc. The proposed porous flow approach divides the entire study area into pores, and it is assumed that pilgrims traverse this network through interconnected vacant pores. The pedestrian data from video sequences (entry time, exit time, and flow) is extracted manually and time series analysis of pore occupancy is done to get an approximate measure of local density. Further, using Poisson regression analysis it was found that both the inflow and the duration of holy dip are significant factors in influencing the number of arrivals into the pore. Since behavioral aspects of a pedestrian is a significant governing factor of crowd dynamics, these microscopic parameters can be used to get a measure of criticality of the system in terms of crowd risk.

@conference{Sekuboyina2017,
title = {A convolutional neural network approach for abnormality detection in wireless capsule endoscopy},
author = {Anjany Kumar Sekuboyina and Surya Teja Devarakonda and Chandra Sekhar Seelamantula},
url = {http://www.rbccps.org/wp-content/uploads/2017/10/07950698.pdf},
doi = {10.1109/ISBI.2017.7950698},
year = {2017},
date = {2017-06-19},
booktitle = {Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging, 18.-21.04.2017, Melbourne (Australia)},
pages = {1057-1060},
abstract = {In wireless capsule endoscopy (WCE), a swallowable miniature optical endoscope is used to transmit color images of the gastrointestinal tract. However, the number of images transmitted is large, taking a significant amount of the medical expert’s time to review the scan. In this paper, we propose a technique to automate the abnormality detection in WCE images. We split the image into several patches and extract features pertaining to each block using a convolutional neural network (CNN) to increase their generality while overcoming the drawbacks of manually crafted features. We intend to exploit the importance of color information for the task. Experiments are performed to determine the optimal color space components for feature extraction and classifier design. We ob-
tained an area under receiver-operating-characteristic (ROC) curve of approximately 0.8 on a dataset containing multiple abnormalities, namely, aphthae, bleeding, chylous cysts, lymphangiectasias, polypoids, stenoses, ulcers, and villous oedema.},
keywords = {Cyber Surgery and Remote Patient Care},
pubstate = {published},
tppubtype = {conference}
}

In wireless capsule endoscopy (WCE), a swallowable miniature optical endoscope is used to transmit color images of the gastrointestinal tract. However, the number of images transmitted is large, taking a significant amount of the medical expert’s time to review the scan. In this paper, we propose a technique to automate the abnormality detection in WCE images. We split the image into several patches and extract features pertaining to each block using a convolutional neural network (CNN) to increase their generality while overcoming the drawbacks of manually crafted features. We intend to exploit the importance of color information for the task. Experiments are performed to determine the optimal color space components for feature extraction and classifier design. We ob-
tained an area under receiver-operating-characteristic (ROC) curve of approximately 0.8 on a dataset containing multiple abnormalities, namely, aphthae, bleeding, chylous cysts, lymphangiectasias, polypoids, stenoses, ulcers, and villous oedema.

In this paper, we propose a Region of Interest (ROI) modulated H.264 video encoder system, based on a distributed object detector-tracker framework, for smart camera networks. Locations of objects of interest, as determined by detector-tracker are used to semantically partition each frame into regions assigned with multiple levels of importance. A distributed architecture is proposed to implement the object detector-tracker framework to mitigate the computational cost. Further, a rate control algorithm with modified Rate-Distortion(RD) cost is proposed to determine Quantization Parameter(QP) and skip decision of Macro Blocks based on their relative levels of importance. Our experiments show that, the proposed system achieves upto 3x reduction in bitrate without significant reduction in PSNR of ROI(head-shoulder region of pedestrians). We also demonstrate the trade-off between total computational cost and compression possible with the proposed distributed detector-tracker framework.

@article{Ramaswamy2017,
title = {A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions},
author = {Arunselvan Ramaswamy and Shalabh Bhatnagar},
url = {http://www.rbccps.org/wp-content/uploads/2018/01/moor.2016.0821.pdf},
doi = {10.1287/moor.2016.0821},
year = {2017},
date = {2017-08-31},
journal = {Mathematics of Operations Research},
volume = {42},
number = {3},
pages = {648-661},
abstract = {In this paper, the stability theorem of Borkar and Meyn is extended to include the case when the mean field is a set-valued map. Two different sets of sufficient conditions are presented that guarantee the “stability and convergence” of stochastic recursive inclusions. Our work builds on the works of Benaïm, Hofbauer and Sorin as well as Borkar and Meyn. As a corollary to one of the main theorems, a natural generalization of the Borkar and Meyn theorem follows. In addition, the original theorem of Borkar and Meyn is shown to hold under slightly relaxed assumptions. As an application to one of the main theorems, we discuss a solution to the “approximate drift problem.” Finally, we analyze the stochastic gradient algorithm with “constant-error gradient estimators” as yet another application of our main result.},
keywords = {Control and Optimisation},
pubstate = {published},
tppubtype = {article}
}

In this paper, the stability theorem of Borkar and Meyn is extended to include the case when the mean field is a set-valued map. Two different sets of sufficient conditions are presented that guarantee the “stability and convergence” of stochastic recursive inclusions. Our work builds on the works of Benaïm, Hofbauer and Sorin as well as Borkar and Meyn. As a corollary to one of the main theorems, a natural generalization of the Borkar and Meyn theorem follows. In addition, the original theorem of Borkar and Meyn is shown to hold under slightly relaxed assumptions. As an application to one of the main theorems, we discuss a solution to the “approximate drift problem.” Finally, we analyze the stochastic gradient algorithm with “constant-error gradient estimators” as yet another application of our main result.

@conference{Dindokar2017,
title = {A meta-graph approach to analyze subgraph-centric distributed programming models},
author = {Ravikant Dindokar and Neel Choudhury and Yogesh Simmhan},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/07840587.pdf},
doi = {10.1109/BigData.2016.7840587},
year = {2017},
date = {2017-02-06},
booktitle = {Proceedings of the 2016 IEEE International Conference on Big Data, 05.-08.12.16, Washington (USA)},
abstract = {Component-centric distributed graph processing models that use bulk synchronous parallel (BSP) execution have grown popular. These overcome short-comings of Big Data platforms like Hadoop for processing large graphs. However, literature on formal analysis of these component-centric abstractions for different graphs, graph partitioning, and graph algorithms is lacking. Here, we propose an coarse-grained analytical approach based on a meta-graph sketch to examine the characteristics of component-centric graph programming models. We apply this sketch to subgraph- and block-centric abstractions, and draw a comparison with vertex-centric models like Google's Pregel. We explore the impact of various graph partitioning techniques on the meta-graph, and the impact of the meta-graph on graph algorithms. This decouples large unwieldy graphs and their partitioning artifacts from their algorithmic analysis. We evaluate our approach for five spatial and powerlaw graphs, four different partitioning strategies, and for PageRank and Breadth First Search algorithms. We show that this novel analytical technique is simple, scalable and yet gives a reliable estimate of the number of supersteps, and the communication and computational complexities of the algorithms for various graphs.},
keywords = {IISc Smart Campus},
pubstate = {published},
tppubtype = {conference}
}

Component-centric distributed graph processing models that use bulk synchronous parallel (BSP) execution have grown popular. These overcome short-comings of Big Data platforms like Hadoop for processing large graphs. However, literature on formal analysis of these component-centric abstractions for different graphs, graph partitioning, and graph algorithms is lacking. Here, we propose an coarse-grained analytical approach based on a meta-graph sketch to examine the characteristics of component-centric graph programming models. We apply this sketch to subgraph- and block-centric abstractions, and draw a comparison with vertex-centric models like Google's Pregel. We explore the impact of various graph partitioning techniques on the meta-graph, and the impact of the meta-graph on graph algorithms. This decouples large unwieldy graphs and their partitioning artifacts from their algorithmic analysis. We evaluate our approach for five spatial and powerlaw graphs, four different partitioning strategies, and for PageRank and Breadth First Search algorithms. We show that this novel analytical technique is simple, scalable and yet gives a reliable estimate of the number of supersteps, and the communication and computational complexities of the algorithms for various graphs.

@conference{Joseph2017,
title = {A model based search method for prediction in model-free Markov decision process},
author = {Ajin George Joseph and Shalabh Bhatnagar},
url = {http://www.rbccps.org/wp-content/uploads/2018/01/07965851.pdf},
doi = {10.1109/IJCNN.2017.7965851},
year = {2017},
date = {2017-07-03},
booktitle = {Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), 14.-19.05.17, Anchorage (USA)},
pages = {170-177},
abstract = {In this paper, we provide a new algorithm for the problem of prediction in the model-free MDP setting, i.e., estimating the value function of a given policy using the linear function approximation architecture, with memory and computation costs scaling quadratically in the size of the feature set. The algorithm is a multi-timescale variant of the very popular cross entropy (CE) method which is a model based search method to find the global optimum of a real-valued function. This is the first time a model based search method is used for the prediction problem. A proof of convergence using the ODE method is provided. The theoretical results are supplemented with experimental comparisons. The algorithm achieves good performance fairly consistently on many benchmark problems.},
keywords = {Control and Optimisation},
pubstate = {published},
tppubtype = {conference}
}

In this paper, we provide a new algorithm for the problem of prediction in the model-free MDP setting, i.e., estimating the value function of a given policy using the linear function approximation architecture, with memory and computation costs scaling quadratically in the size of the feature set. The algorithm is a multi-timescale variant of the very popular cross entropy (CE) method which is a model based search method to find the global optimum of a real-valued function. This is the first time a model based search method is used for the prediction problem. A proof of convergence using the ODE method is provided. The theoretical results are supplemented with experimental comparisons. The algorithm achieves good performance fairly consistently on many benchmark problems.

@article{Gayarthi2017,
title = {A review of studies on understanding crowd dynamics in the context of crowd safety in mass religious gatherings},
author = {H. Gayarthi and P. M. Aparna and Ashish Verma},
url = {http://www.rbccps.org/wp-content/uploads/2017/10/1-s2.0-S2212420917302212-main.pdf},
doi = {10.1016/j.ijdrr.2017.07.017},
year = {2017},
date = {2017-08-03},
journal = {International Journal of Disaster Risk Reduction},
abstract = {Understanding the principles and applications of crowd dynamics in mass gatherings is very important, specifically with respect to crowd risk analysis and crowd safety. Historical trends from India and other countries suggest that the stampedes in mass gatherings, especially in religious events occur frequently highlighting the importance of studying the crowd behaviour more scientifically. This is required to support appropriate and timely crowd management principles, in the planning of crowd control measures and provision of early warning systems at mass gatherings. Common pedestrian behaviours in crowd like group formation, self-organization, leader follower effect, queue formation and bottleneck conditions have substantial influence on crowd dynamics. It is important not to let a single aspect go overlooked with respect to mass gatherings since it can lead to major stampedes. Kumbh Mela, one of the largest mass religious gatherings in the world, features these different crowd scenarios observed often in the same event area and thus provides a unique opportunity to study the crowd behaviour in a holistic way. Understanding these pedestrian behaviours and having a clear understanding of the normal behaviour may provide opportunities to change crowd dynamics and overcome the adverse effects resulting in safer mass religious gatherings in future. This paper provides an exhaustive review of the current understanding of crowd dynamics and explores the modelling techniques that are available to enhance crowd safety. The purpose of this literature review is to improve the understanding of crowd dynamics,and highlight the research gaps in the context of crowd safety in mass religious gatherings like Kumbh Mela.},
keywords = {The Kumbh Mela Experiment},
pubstate = {published},
tppubtype = {article}
}

Understanding the principles and applications of crowd dynamics in mass gatherings is very important, specifically with respect to crowd risk analysis and crowd safety. Historical trends from India and other countries suggest that the stampedes in mass gatherings, especially in religious events occur frequently highlighting the importance of studying the crowd behaviour more scientifically. This is required to support appropriate and timely crowd management principles, in the planning of crowd control measures and provision of early warning systems at mass gatherings. Common pedestrian behaviours in crowd like group formation, self-organization, leader follower effect, queue formation and bottleneck conditions have substantial influence on crowd dynamics. It is important not to let a single aspect go overlooked with respect to mass gatherings since it can lead to major stampedes. Kumbh Mela, one of the largest mass religious gatherings in the world, features these different crowd scenarios observed often in the same event area and thus provides a unique opportunity to study the crowd behaviour in a holistic way. Understanding these pedestrian behaviours and having a clear understanding of the normal behaviour may provide opportunities to change crowd dynamics and overcome the adverse effects resulting in safer mass religious gatherings in future. This paper provides an exhaustive review of the current understanding of crowd dynamics and explores the modelling techniques that are available to enhance crowd safety. The purpose of this literature review is to improve the understanding of crowd dynamics,and highlight the research gaps in the context of crowd safety in mass religious gatherings like Kumbh Mela.

We present the first sufficient conditions that guarantee stability of two-timescale stochastic approxima- tion schemes. Our analysis is based on the ordinary differential equation (ODE) method and is an extension of the results in Borkar and Meyn (2000) for single-timescale schemes. As an application of our result, we show the stability of iterates in a two-timescale stochastic approximation scheme arising in reinforce- ment learning.

@article{Diddigi2017b,
title = {A unified decision making framework for supply and demand management in microgrid networks},
author = {Raghuram Bharadwaj Diddigi and D. Sai Koti Reddy and Krishnasuri Narayanam and Shalabh Bhatnagar},
url = {http://www.rbccps.org/wp-content/uploads/2018/12/1711.05078.pdf},
year = {2017},
date = {2017-11-14},
journal = {arXiv: Computer Science},
abstract = {This paper considers two important problems - on the supply-side and demand-side respectively and studies both in a unified framework. On the supply side, we study the problem of energy sharing among microgrids with the goal of maximizing profit obtained from selling power while meeting customer demand. On the other hand, under shortage of power, this problem becomes one of deciding the amount of power to be bought with dynamically varying prices. On the demand side, we consider the problem of optimally scheduling the time-adjustable demand - i.e., of loads with flexible time windows in which they can be scheduled. While previous works have treated these two problems in isolation, we combine these problems together and provide for the first time in the literature, a unified Markov decision process (MDP) framework for these problems. We then apply the Q-learning algorithm, a popular model-free reinforcement learning technique, to obtain the optimal policy. Through simulations, we show that our model outperforms the traditional power sharing models. },
keywords = {Smart Grid},
pubstate = {published},
tppubtype = {article}
}

This paper considers two important problems - on the supply-side and demand-side respectively and studies both in a unified framework. On the supply side, we study the problem of energy sharing among microgrids with the goal of maximizing profit obtained from selling power while meeting customer demand. On the other hand, under shortage of power, this problem becomes one of deciding the amount of power to be bought with dynamically varying prices. On the demand side, we consider the problem of optimally scheduling the time-adjustable demand - i.e., of loads with flexible time windows in which they can be scheduled. While previous works have treated these two problems in isolation, we combine these problems together and provide for the first time in the literature, a unified Markov decision process (MDP) framework for these problems. We then apply the Q-learning algorithm, a popular model-free reinforcement learning technique, to obtain the optimal policy. Through simulations, we show that our model outperforms the traditional power sharing models.

We present new algorithms for simulation op- timization using random directions stochastic approxima- tion (RDSA). These include first-order (gradient) as well as second-order (Newton) schemes. We incorporate both continuous-valued as well as discrete-valued perturbations into both types of algorithms. The former are chosen to be independent and identically distributed (i.i.d.) symmet- ric uniformly distributed random variables (r.v.), while the latter are i.i.d. asymmetric Bernoulli r.v.s. Our Newton al- gorithm, with a novel Hessian estimation scheme, requires N -dimensional perturbations and three loss measurements per iteration, whereas the simultaneous perturbation New- ton search algorithm of [1] requires 2N-dimensional per- turbations and four loss measurements per iteration. We prove the asymptotic unbiasedness of both gradient and Hessian estimates and asymptotic (strong) convergence for both first-order and second-order schemes. We also pro- vide asymptotic normality results, which in particular estab- lish that the asymmetric Bernoulli variant of Newton RDSA method is better than 2SPSA of [1]. Numerical experiments are used to validate the theoretical results.

@conference{Joseph2017c,
title = {An incremental fast policy search using a single sample path},
author = {Ajin George Joseph and Shalabh Bhatnagar},
doi = {10.1007/978-3-319-69900-4_1},
year = {2017},
date = {2017-11-01},
booktitle = {Proceedings of the 7th International Conference on Pattern Recognition and Machine Intelligence (PReMI), 05.-08.12.17, Kolkata (India)},
pages = {3-10},
series = {Lecture Notes in Computer Science},
abstract = {In this paper, we consider the control problem in a reinforcement learning setting with large state and action spaces. The control problem most commonly addressed in the contemporary literature is to find an optimal policy which optimizes the long run γ-discounted transition costs, where γ∈[0,1). They also assume access to a generative model/simulator of the underlying MDP with the hidden premise that realization of the system dynamics of the MDP for arbitrary policies in the form of sample paths can be obtained with ease from the model. In this paper, we consider a cost function which is the expectation of a approximate value function w.r.t. the steady state distribution of the Markov chain induced by the policy, without having access to the generative model. We assume that a single sample path generated using a priori chosen behaviour policy is made available. In this information restricted setting, we solve the generalized control problem using the incremental cross entropy method. The proposed algorithm is shown to converge to the solution which is globally optimal relative to the behaviour policy.},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {conference}
}

In this paper, we consider the control problem in a reinforcement learning setting with large state and action spaces. The control problem most commonly addressed in the contemporary literature is to find an optimal policy which optimizes the long run γ-discounted transition costs, where γ∈[0,1). They also assume access to a generative model/simulator of the underlying MDP with the hidden premise that realization of the system dynamics of the MDP for arbitrary policies in the form of sample paths can be obtained with ease from the model. In this paper, we consider a cost function which is the expectation of a approximate value function w.r.t. the steady state distribution of the Markov chain induced by the policy, without having access to the generative model. We assume that a single sample path generated using a priori chosen behaviour policy is made available. In this information restricted setting, we solve the generalized control problem using the incremental cross entropy method. The proposed algorithm is shown to converge to the solution which is globally optimal relative to the behaviour policy.

Street light poles will be a key enabler for a smart city's hardware infrastructure, thanks to their ubiquity throughout the city as well as access to power. We propose an IoT test bed around light poles for the city, with a modular hardware and software architecture to enable experimentation with various technologies.

@article{Ramaswamy2017b,
title = {Analysis of gradient descent methods with non-diminishing bounded errors },
author = {Arunselvan Ramaswamy and Shalabh Bhatnagar},
url = {http://www.rbccps.org/wp-content/uploads/2018/01/08016343.pdf},
doi = {10.1109/TAC.2017.2744598},
year = {2017},
date = {2017-08-24},
journal = {IEEE Transactions on Automatic Control},
abstract = {The main aim of this paper is to provide an analysis of gradient descent (GD) algorithms with gradient errors that do not necessarily vanish, asymptotically. In particular, sufficient conditions are presented for both stability (almost sure boundedness of the iterates) and convergence of GD with bounded, (possibly) non-diminishing gradient errors. In addition to ensuring stability, such an algorithm is shown to converge to a small neighborhood of the minimum set, which depends on the gradient errors. It is worth noting that the main result of this paper can be used to show that GD with asymptotically vanishing errors indeed converges to the minimum set. The results presented herein are not only more general when compared to previous results, but our analysis of GD with errors is new to the literature to the best of our knowledge. Our work extends the contributions of Mangasarian and Solodov, Bertsekas and Tsitsiklis and Tadic and Doucet. Using our framework, a simple yet effective implementation of GD using simultaneous perturbation stochastic approximations (SP SA), with constant sensitivity parameters, is presented. Another important improvement over many previous results is that there are no “additional” restrictions imposed on the step-sizes. In machine learning applications where step-sizes are related to learning rates, our assumptions, unlike those of other papers, do not affect these learning rates. Finally, we present experimental results to validate our theory.},
keywords = {Control and Optimisation},
pubstate = {published},
tppubtype = {article}
}

The main aim of this paper is to provide an analysis of gradient descent (GD) algorithms with gradient errors that do not necessarily vanish, asymptotically. In particular, sufficient conditions are presented for both stability (almost sure boundedness of the iterates) and convergence of GD with bounded, (possibly) non-diminishing gradient errors. In addition to ensuring stability, such an algorithm is shown to converge to a small neighborhood of the minimum set, which depends on the gradient errors. It is worth noting that the main result of this paper can be used to show that GD with asymptotically vanishing errors indeed converges to the minimum set. The results presented herein are not only more general when compared to previous results, but our analysis of GD with errors is new to the literature to the best of our knowledge. Our work extends the contributions of Mangasarian and Solodov, Bertsekas and Tsitsiklis and Tadic and Doucet. Using our framework, a simple yet effective implementation of GD using simultaneous perturbation stochastic approximations (SP SA), with constant sensitivity parameters, is presented. Another important improvement over many previous results is that there are no “additional” restrictions imposed on the step-sizes. In machine learning applications where step-sizes are related to learning rates, our assumptions, unlike those of other papers, do not affect these learning rates. Finally, we present experimental results to validate our theory.

@conference{Shyam2017,
title = {Attentive recurrent comparators},
author = {Pranav Shyam and Shubham Gupta and Ambedkar Dukkipati},
url = {http://www.rbccps.org/wp-content/uploads/2018/01/shyam17a.pdf},
year = {2017},
date = {2017-08-11},
booktitle = {Proceedings of the 34th International Conference on Machine Learning (ICML 2017), 06.-11.08.17, Sydney (Australia)},
volume = {70},
pages = {3173-3181},
abstract = {Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a dynamic representation space and use it for one- shot learning. In the task of one-shot classifi- cation on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1.5%. This represents the first super-human re- sult achieved for this task with a generic model that uses only pixel information.},
keywords = {CyberGut: A Bio-CPS approach to understand gut biology},
pubstate = {published},
tppubtype = {conference}
}

Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a dynamic representation space and use it for one- shot learning. In the task of one-shot classifi- cation on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1.5%. This represents the first super-human re- sult achieved for this task with a generic model that uses only pixel information.